Land Surface Physics - INPEsatelite.cptec.inpe.br/repositorio9/wcrp/... · 2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil,

2nd WCRP Summer School on Climate Model Development Cachoeira Paulista – SP, Brazil, 22-31 January 2017 [email protected]

Land Surface Physics

Michael Ek, and a number of collaborators!

Environmental Modeling Center (EMC) National Centers for Environmental Prediction (NCEP)

College Park, MD, USA

National Weather Service (NWS) National Oceanic and Atmospheric Administration (NOAA)

2nd WCRP Summer School on Climate Model Development Center for Weather Forecasts and Climate Studies

Brazilian National Institute for Space Research Cachoeira Paulista – SP, Brazil

22-31 January 2018

1/50


• Role of Land Surface Models (LSMs)

• Land-surface model requirements: physics &

model parameters, atmospheric forcing, land data sets, initial land states, and land data assimilation

• Land Applications for Weather & Climate

• Testing and Validation

• Land Models in a fully-coupled Earth System

• Land-Atmosphere Interactions

• Summary

Outline

2/50


Why Land? Predictability and Prediction

• To have an effect, must have:

1. Memory of initial land states.

2. Sensitivity of fluxes to land states, atmosphere to fluxes.

3. Sufficient variability

• Land states (namely soil moisture, snow, vegetation) can provide predictability in the window between deterministic Weather and Climate (Ocean‐Atmosphere) time scales.

Paul Dirmeyer (George Mason Univ.)

Time ~10 days ~2 months

Ocean (“Climate”)

Pred

icta

bil

ity Atmosphere

(Weather)

Land

…and good models!

…and accurate analyses!

3/50


Traditionally, from the perspective of Numerical Weather Prediction (NWP) or (coupled atmosphere-ocean-land-ice) Climate Modeling, a land-surface model provides quantities as boundary conditions:

• Surface sensible heat flux

• Surface latent heat flux (evapotranspiration)

• Upward longwave radiation (or skin temperature and surface emissivity)

• Upward (reflected) shortwave radiation (or surface albedo, including snow effects)

• Surface momentum exchange

• Surface water budget

Role of Land Models

4/50


seasonal storage

Stephens et al 2012

• We must more properly represent & close the (surface) energy budget and hydrological cycle (water budget) to provide surface boundary conditions in weather and climate models as they expand their role in more fully-coupled Earth System models.

• It’s a careful “bookkeeping job” for energy and water, as well as BioGeoChem cycles. Particularly importance for climate models.

Atmospheric Energy and Water Budgets

USGS

5/50


Land modeling example:

• Products and models are integrated & consistent throughout time & space, as well as across forecast application & domain.

Static vegetation, e.g. climatology

or realtime observations

Dynamic vegetation, e.g. plant growth

Dynamic ecosystems, e.g. changing

land cover

Weather & Climate: a “Seamless Suite”

6/50


Land Modeling History: NWP perspective from NCEP, but similarly ignored in climate & other weather models initially

• 1960s (6-Layer Primitive Equations model): land surface ignored, aside from terrain height and surface friction effects.

• 1970s (Limited-Area Fine-Mesh model): land surface ignored.

• Late 1980s (Nested-Grid Model): 1st land surface model (LSM): - Single layer soil slab (Deardorff “force-restore” soil model). - No explicit vegetation treatment. - Temporally fixed surface wetness factor. - Diurnal cycle treated (and diurnal ABL) with diurnal surface radiation. - Surface albedo, surface skin temperature, surface energy balance. - Snow cover (but not depth).

• Early1990s (Global MRF): Oregon State University LSM: - Multi-layer soil column (2-layers). - Explicit annual cycle of vegetation effects. - Snow pack physics (snowdepth, SWE).

• Mid 1990s (Meso Eta model): Noah LSM replaces force-restore.

• Mid 2000s (Global Model: GFS): Noah LSM replaces OSU LSM.

• Mid 2000s (Meso Model: WRF): Unified Noah LSM with NCAR.

• 2010s: “Noah-MP” with NCAR Noah model development group.

7/50

At least it’s (generally) no longer: “Land doesn’t really matter too much …unless there’s an unexplained bias.” Land isn’t just a boundary condition!


Land Models for Weather and Climate

Weather versus Climate (change) considerations:

• Static vegetation, vs dynamic vegetation (growth), versus dynamic ecosystems (plant succession), and biogeochemical cycles with CO2-based photo-synthesis, different crops, C3, C4, CAM vegetation.

• Longer time-scales spin-up for deeper soils and groundwater, regions with “slow” hydrological cycle (especially arid, cold regions), carbon stores.

• Land-use change (observed/assimilated vs model-led), e.g. harvest, fires, expansion of urban areas.

• Human influences, e.g. irrigation/reservoirs.

• Bookkeeping of energy, water, other biogeochemical budgets, e.g. heat content transported by rivers.

• Should be a seamless weather & climate connection. 8/50


Land Models for Weather and Climate

NOAA/ESRL RUC

UKMO JULES ECMWF TESSEL

GFDL LM4

NCAR CLM

UW/Princeton VIC

NCEP-NCAR Noah NASA Catchment

NWS Nat’l Water

BATS SiB

Noah-MP

Weather Seasonal Prediction Climate Change

• A sampling –use dicates complexity and processes simulated.

9/50


• Four soil layers (shallower near-surface).

• Numerically efficient surface energy budget.

• Jarvis-Stewart “big-leaf” canopy conductance with associated veg parameters.

• Canopy interception.

• Direct soil evaporation.

• Soil hydraulics and soil parameters.

• Vegetation-reduced soil thermal conductivity.

• Patchy/fractional snow cover effect on sfc fluxes.

• Snowpack density and snow water equivalent.

• Freeze/thaw soil physics.

Unified NCEP-NCAR Noah Land Model

• Noah for NWP & seasonal prediction, coupled with NCEP short-range NAM, medium-range GFS, seasonal CFS, and HWRF, uncoupled NLDAS & GLDAS, etc.

10/50


• To provide proper boundary conditions, land surface model (LSM) must have:

- Appropriate physics to represent land-surface processes (for relevant time/spatial scales) and associated LSM model parameters.

- Required atmospheric forcing to drive LSM.

-Corresponding land data sets, e.g. land use/land cover (vegetation type), soil type, surface albedo, snow cover, surface roughness, etc.

- Proper initial land states, analogous to initial atmospheric conditions, though land states may carry more “memory” (e.g. especially in deep soil moisture), similar to ocean SSTs & heat content.

• Land data sets & initial land states may “overlap”, that is, some land data sets (assumed) static while others “evolve”, with use of many remotely-sensed data sets/products, also for atmospheric forcing, and for land model validation.

Land Model Requirements

11/50


Land: Coupled with Atmospheric Model

Momentum

Continuity

Eq. State

Thermodynamic

Land: Source/Sink Terms

12/50


Soil Moisture (Θ):

• “Richard’s Equation”; DΘ (soil water diffusivity) and KΘ

(hydraulic conductivity), are nonlinear functions of soil moisture and soil type (e.g. Cosby et al 1984, van Genuchten, 1980); FΘ

is a source/sink term for precipitation/evapotranspiration.

Soil Temperature (T):

• CT (thermal heat capacity) and KT (soil thermal conductivity; e.g. Johansen 1975), non-linear functions of soil/type; soil ice = fct(soil type/temp./moisture).

Canopy water (Cw):

• P (precipitation) increases Cw, while Ec (canopy water evaporation) decreases Cw.

Land Physics: Basic Prognostic Equations

13/50


Land Physics: Flux Boundary Conditions

soil

canopy

canopy water

Transpiration Canopy Water Evaporation

Latent heat flux (Evapotranspiration)

Direct Soil Evaporation

from soil surface/canopy

Sensible heat flux

to canopy/soil surface

Soil heat flux

sensible (H) and latent (LE) heat fluxes, and soil heat flux (G).

•Surface fluxes balanced by net radiation (Rn) = sum of incoming and outgoing solar and terrestrial radiation, with vegetation important in energy partition between turbulent

Emitted longwave

snow

Snowpack & frozen soil physics

14/50

Hyd

ro

log

y


Land Physics: Model Parameters

• Surface momentum roughness dependent on vegetation/land-use type and vegetation fraction.

• Stomatal control dependent on vegetation type, direct effect on transpiration.

• Depth of snow (snow water equivalent) for deep snow and assumption of maximum snow albedo is a function of vegetation type.

• Heat transfer through vegetation and into the soil a function of green vegetation fraction (coverage) and leaf area index (density).

• Soil thermal and hydraulic processes highly dependent on soil type (vary by orders of magnitude).

15/50


Atmospheric Forcing to Land Model

• Atmospheric forcing from a parent atmospheric model (analysis or reanalysis) and/or from in situ or remotely-sensed observations, where precipitation is quite important for LSMs.

Precipitation Incoming Solar

Wind Speed

Air Pressure

Air Temperature

Downward Longwave

Specific Humidity

16/50


• Global Land Data Assimilation System (GLDAS) used in NCEP Climate Forecast System (CFS) relies on “blended” precipitation product, function of: Satellite-estimated precipitation (CMAP) (heaviest weight in tropics where gauges sparse); Surface gauge network (heaviest in mid-latitudes); Model-based Global Data Assimilation System (high latitudes where obs are scarce).

• For other applications (e.g. higher-resolution LDAS over CONUS), temporally dis-aggregate gauge data using radar precip estimates (“Stage IV)”.

Atmospheric Forcing: Precipitation

CMAP (merged rain

gauge & CMORPH satellite obs.)

Surface gauge GDAS (model)

Jesse Meng (NCEP/EMC), Pingping Xie (NCEP/CPC)

Gauge+Radar

17/50


Soil Type (1-km, STATSGO-FAO)

Land-Use/Vegetation (1-km, IGBP-MODIS)

Max.-Snow Albedo (1-km, UAz-MODIS)

Snow-Free Albedo (monthly, 1-km, Boston Univ.-MODIS)

July Jan

• Fixed annual/monthly/weekly climatologies, or near real-time observations; some quantities may be assimilated into LSMs, e.g. soil moisture, snow, greenness as initial land states.

Land Data Sets

Green Vegetation Fraction (monthly, 1/8-deg, NESDIS/AVHRR)

July Jan

May change, e.g. urban,

desertification

Quasi-static

Changes with changing veg type/land-use

Greenness may be ahead of/behind

climatology

Actual albedo may better reflect current

land states

18/50


Land Data Sets: Green Vegetation Fraction (GVF) and Wildfire Effects

Weizhong Zheng and Yihua Wu, NCEP/EMC, Marco Vargas et al, NESDIS/STAR

Current AVHRR 5-year climatology

• Use of near realtime GVF leads to better partition between surface heating & evaporation --> impacts surface energy budget, ABL evolution, clouds/convection.

VIIRS near- realtime

• Wildfires affect weather and climate systems: (1) atmospheric circulations, (2) aero-sols/clouds, (3) land surface states (GVF, albedo & surface temperature, etc.) impact on sfc energy budget, etc. Consistency with “burned” & other products, e.g GVF.

19/50


• Land state initial conditions are necessary for NWP & climate models, and must be consistent with land model used in a given weather or climate model, i.e. from same cycling land model.

• Land states spun up in a given NWP or climate model cannot be used directly to initialize another model without rescaling because of differing land model soil moisture climatology.

• In seasonal (and longer) climate simulations, land states are cycled, and some land data set quantities may be simulated (and therefore assimilated), e.g. green vegetation fraction & leaf area index, and even land-use type (evolving ecosystems).

Initial Land States: Soil Moisture

May Soil Moisture Climatology from 30-year NCEP Climate Forecast System Reanalysis (CFSR), spun up from Noah land model coupled with CFS.

Jesse Meng (NCEP/EMC)

20/50


Initial Land States: Snow

Air Force Weather Agency snow cover & depth

• In addition to soil moisture: the land model provides surface skin temperature, soil temperature, soil ice, canopy water, and snow depth & snow water equivalent.

National Ice Center snow cover

21/50


JPL Center for Climate Sciences Summer School 15-19 August 2016 [email protected]

• Data assimilation: observations of actual system are incorpo-rated into model state of numerical model of that system.

• Minimization of a "cost function” has the effect of making sure that analysis does not drift too far away from observations and forecasts, and requires observation and model error estimates.

Land Data Assimilation (LDA)

22/50

Thanks, Google! Analysis minimizes a combination of distances:



C. Peters-Lidard et al (NASA)

Developing Land Data Assimilation Capabilities

Figure 1: Snow water equivalent (SWE)

based on Terra/MODIS and Aqua/AMSR-E.

Future observations will be provided by

JPSS/VIIRS and DWSS/MIS. Jiarui Dong

(NCEP)

Snow

Figure 2: Annual average precipitation from

1998 to 2009 based on TRMM satellite

observations. Future observations will be

provided by GPM.

Precipitation

Figure 5: Current lakes and reservoirs

monitored by OSTM/Jason-2. Shown are

current height variations relative to 10-year

average levels. Future observations

will be provided by SWOT.

Surface Water

Figure 4: Changes in annual-average

terrestrial water storage (sum of groundwater,

soil water, surface water, snow, and ice)

between 2009 and 2010, based on GRACE

satellite observations. Future observations will

be provided by GRACE-II.

Terr. Water Storage

Snow and Soil Moisture Active Areas of Land DA Testing at NCEP

Figure 3: NESDIS “SMOPS” daily product

blends soil moisture from a number of sensors,

including SMOS, SMAP, ASCAT, AMSR-2.

Positive impact on global precip. forecasts.

Weizhong Zeng (NCEP), Xiwu Zhan (NESDIS).

Soil Moisture

23/50

Surface Water & Ocean Topography (SWOT)

Gravity Recovery and Climate Experiment

(GRACE)

e.g. Soil Moisture Active Passive (SMAP)

e.g. Global Precipitation Measurement (GPM)

e.g. Snow Cover from Moderate Res. Imaging

Spectroradiometer (MODIS)



24/50

Quantity Remotely-Sensed Assimilation Validation

Forc

ing Precipitation x - -

Incoming solar x - -

Downward longwave x - -

Wind, Temperature, Humidity, Pressure x - -

Land D

ata

Sets

Albedo, emissivity monthly - -

Maximum snow albedo static - -

Vegetation fraction/Leaf Air Index weekly x -

Vegetation type/land-use quasi-static - -

Soil type static - -

Land s

tate

s Soil moisture x x x

Surface temperature (LST) x - x

Soil temperature - - x

Snow depth, density, cover x x x

Canopy water - - -

Surface fluxes x - x

BioGeoChemical Cycles (e.g. carbon) x x x

Hydrology: streamflow, groundwater, water level x x x

Remotely-sensed data sets: Land Summary (incomplete/evolving)



Space Weather

ENLIL

North American Land Surface Data

Assimilation System

Noah Land Surface Model

NAM/NAM-nests

NMMB Noah land model 3

D-H

ybri

d-

VA

R D

A

Regional Hurricane

GFDL HWRF/Noah

3D

-En

-Var

D

A

Regional Bays •Great Lakes (POM) •N Gulf of Mexico

(FVCOM) •Columbia R.

(SELFE) •Chesapeake (ROMS)

•Tampa (ROMS) •Delaware (ROMS)

Ecosystem EwE

Global Spectral Noah Land model 4

D-E

n-V

ar

DA

Global Forecast System (GFS)

Global Ensemble Forecast System (GEFS)

21 GFS Members

North American Ensemble Forecast System

GEFS, Canadian Global Model

Dispersion HYSPLIT

WRF ARW

3D

-VA

R

DA

High-Res RR (HRRR)

Air Quality

CMAQ

WRF-ARW & NMMB

High Res Windows

Climate Forecast System (CFS)

3D

-Var

D

A

GFS, MOM4, GLDAS/Noah Land,

Sea Ice

Nearshore Waters SURGE: SLOSH

ESTOFS: ADCIRC NWPS: WWIII

August 2016

Regional Climate Data Assimil. Syst.

Eta-32/NARR

National Water Model

Noah-MP

3D

-VA

R

DA

WRF ARW

Rapid Refresh

WRF-ARW & NMMB 13 members each

Short-Range Ensemble Forecast 26 members

NEMS Aerosol Global Component

(NGAC) GFS & GOCART

Ocean (RTOFS) HYCOM

Waves WaveWatch III

Sea-ice

Bill Lapenta, NCEP

RUCLSM

Land Applications for Weather and Seasonal Climate: NOAA’s Operational Numerical Guidance Suite

25/50

RUCLSM


• Uses Noah land model forced with Climate Forecast System (CFS) atmospheric data assimilation cycle output, “blended” precipitation (gauge, satellite & model), “semi-coupled” –daily updated land states, with Snow cycled or assimilated (IMS snow cover, AFWA depth).

• 30-year land climatology: energy/water budgets (below).

• 30-year re-runs to test updated forcing, land data sets, physics.

Drive LSM “Offline”: Global Land Data Assimilation System (GLDAS)

Jan (top), July (bottom) Climatology from 30-year NCEP CFS Reanalysis (Precip, Evap, Runoff [mm/day]; Soil Moisture, Snow [mm])

Runoff Precipitation Evaporation Soil Moisture Snow

Rongqian Yang, Jesse Meng (NCEP/EMC)

26/50


US Mid-west Drought

Youlong Xia (NCEP/EMC)

California Drought

North American LDAS Soil Moisture Monitoring

Ensemble mean total column soil moisture anomaly March 2012 – December 2013

27/50


• Standard validation of NWP & climate models uses near-surface observations, e.g. routine weather observations of air temp., dew point and relative humidity, 10-meter wind, along with upper-air, precipitation, etc.

Land Model Testing and Validation

• To more fully validate land models/processes, surface fluxes and soil states (soil moisture, etc) also used.

• Diurnal composites (e.g. monthly/seasonal) used to assess systematic model biases (averaging out transient atmospheric conditions), and suggest land physics upgrades.

28/50


Testing & Validation: Land and related issues

Low-level biases in winds, temperature, and humidity are influenced in part by the land surface via biases in surface fluxes exchanged with the atmospheric model, with effect on boundary-layer evolution, and influence on clouds/convection/precipitation.

Improving the proper diurnal/seasonal partition of surface energy budget between sensible, latent, soil heat flux, outgoing longwave radiation, and effect on water budget, requires:

• Improved vegetation physics/parameters to calculate ET.

• Better soil physics/properties address surface heterogeneity.

• Improved snow physics (melt/freeze, densification).

• Surface-layer physics, especially nighttime/stable conditions, and interaction with the surface & atmospheric boundary layer.

• Remote sensing of different initial land states, e.g. near-real-time vegetation; corresponding data assimilation of these land states, e.g. snow, soil moisture, green vegetation fraction.

• Improved forcing for land models, especially precipitation and downward radiation; requires enhanced downscaling techniques.

29/50


Ocean, Waves

Microphysics

Boundary-Layer

Radiation

Clouds & convection

Simulators Simulators: test submodel parameterizations at process level, e.g. radiation-only, land-only, ...

Land

Sea-ice

Surface-layer

Testbed data sets to develop, drive & validate submodels: observations, models, idealized, with “benchmarks” before adopting changes.

More efficient model development, community engagement, R2O/O2R and computer usage. S

URFACE

Submodel interactions, with benchmarks.

Interaction tests

Limited-area/3-D (convection) with benchmarks.

Limited-area Column tests

Full columns, with benchmarks.

Regional & Global

Regional & global NWP & seasonal climate, with benchmarks.

e.g. several WMO WCRP/GEWEX activities, NCAR/NOAA Global Model Testbed (GMTB) 30/50

Testing & Validation: Simple-to-More Complex Hierarchy of Model Parameterization Development


Testing & Validation: Model components

• Use surface fluxes (e.g. sensible/latent heat, momentum) to evaluate land-surface physics formulations and parameters, e.g. invert formulations to infer heat and momentum exchange coefficients, and canopy conductance (below) from data sets.

• Action: adjust thermal, drag, canopy parameters.

model (solid, dashed lines)

observations (symbols)

31/50


E E

E

D

B

B

D G M G G C

W S

P

W

E E B D

E – Evergreen Needleleaf B – Evergreen Broadleaf D – Deciduous Broadleaf

M – Mixed Forest G - Grassland C – Cropland

W – Woody Savanna S – Savanna

P – Permanent Wetlands

The PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER)

Testing & Validation: Land Model Benchmarking

• GEWEX/Global Land/Atmos. System Study (GLASS) project. • Evaluate performance of land-surface models in the context of pre-defined metrics for a number of flux sites worldwide.

• Land models should out-perform empirical models, and persistence (for NWP models), climatology (for climate models).

• Identify systematic biases for model development/validation. • Extend to 2-D eval. over regional/global domains, include water.

32/50 Martin Best (UKMO), Gab Abramowitz (UNSW) et al.

PALS example: CABLE (BOM/Aust.) land model, Bondville, IL, USA (cropland), 1997-2006, avg diurnal cycles.

LE

Rn G

H spring

summer

autumn

winter

model obs


Testing & Validation: Column Model Testing

• GEWEX/GLASS-GASS (Global Atmospheric System Studies) Project: Diurnal land-atmosphere coupling experiment (DICE).

• DICE-1: Study the interactions between the land-surface and atmospheric boundary layer, and assess feedbacks.

• DICE-Over-Ice: Study the interactions between the ice/snow-surface and atmospheric boundary layer under conditions of strong stability, and assess feedbacks.

• Extend to many other regions/seasons across the globe.

Dome C - Antarctica

DICE-Over-Ice

Martin Best, Adrian Lock (UKMO) et al.

DICE-1

Southern Great Plains, USA (CASES-99 Experiment)

33/50


Land Models in Earth Systems

In a more fully-coupled Earth System, this role involves Weather & Climate connections to:

• Hydrology: soil moisture & ground water/water tables, irrigation and groundwater extraction, water quality, streamflow and river discharge to oceans, drought/flood, lakes, and reservoirs/human mgmt.

• Biogeochemical cycles: application to ecosystems, both terrestrial & marine, dynamic vegetation and biomass, carbon budgets, etc.

• Air Quality: interaction with boundary-layer, biogenic emissions, VOC, dust/aerosols, etc.

More constraints, i.e. must close energy and water budgets, and those related to BGC cycles and air/water quality.

34/50

Get the right answers for the right reasons!


• Because of land surface heterogeneity, a model grid may comprise sub-atmospheric-grid-scale land “tiles”, e.g. forest, grass, crop, water, etc, O(1-4km).

• Coarser-resolution atmospheric forcing to land. • Aggregate flux input to “parent” atmospheric model.

surface tiles with different fluxes

• When blending height is greater than atmos-pheric boundary-layer depth, cannot simply use aggregate flux.

Aggregate surface flux

“blending” height

Tiled Land Grids and Scale Dependence

• Land tiles may be coupled with yet-higher resolution hydrology-tiles to be connected to groundwater and river-routing scheme.

35/50


Atmospheric Forcing

Hydrology: River-routing, Groundwater

Ensemble mean daily streamflow anomaly

Hurricane Irene and Tropical Storm Lee, 20 August – 17 September 2011

Superstorm Sandy, 29 October – 04 November 2012

Colorado Front Range Flooding, September 2013

Groundwater

Surface flow

Saturated subsurface flow

36/50

NWS high-resolution National Water Model

1-km land, 250m hydro


Yihua Wu (NCEP/EMC)

• Thousands of lakes on scale of 1-4km not resolved by SST analysis -> greatly influence surface fluxes; explicit vs subgrid.

• Freshwater lake “FLake” model (Dmitrii Mironov,DWD).

- Two-layer. - Atmospheric forcing inputs.

- Temperature & energy budget.

- Mixed-layer and thermocline.

- Snow-ice module - Specified depth/turbidity.

- Used in COSMO, HIRLAM, NAM (regional), and global ECMWF, CMC, UKMO.

Lakes

37/50

“It’s not ocean, so let land modelers deal with it.”


Ecosystems, Water and Air Quality

• Sea Nettle Forecasting

Salinity

SST Likelihood of Chrysaora

Habitat Model

• Hydro-Coastal Linkage

Nearshore Freshwater

“RTOFS”

Control “RTOFS”

Gulf of Mexico

Observed Salinity

• Land sources of dust and aerosols, mixed through ABL.

38/50


Human Influences/Water Management

Irrigation Groundwater

Extraction Reservoirs

Urban areas/model Land-cover change/deforestation

• Proper initial conditions (e.g. via remote sensing), and improved land model physics parameterizations.

39/50


Land-Atmosphere Interaction

• Land-atmosphere interaction and coupling strength remain weak links in current land-surface and atmospheric/earth-system prediction models.

• Coupling strength affects surface fluxes, so important for weather and climate.

•We need to understand many land & atmospheric processes and interactions, with proper representation in weather and climate models...

• ... e.g. soil moisture/evaporative flux to the atmosphere (clouds/convection), hydrology/river-flow and connection to oceans, dust/suspension & surface chemistry interactions, plants/PAR, etc.

• Coupling begins locally.

40/50



Large-scale: Soil Moisture-Precipitation “Hot spots”

• Modeling study: Areas where soil moisture changes can affect rainfall. R. Koster* et al (2004), NASA/GSFC *author of The Swenson

Code

41/50


Land-Atmosphere Interactions

Paul Dirmeyer (George Mason Univ.), Joe Santanello (NASA/GSFC)

Land-Atmosphere Feedbacks “Stand on 2 Legs”

• Terrestrial – When/where does soil moisture (vegetation, soil, snow, etc.) control the partitioning of net radiation into sensible and latent heat flux (and soil heat flux)?

• Atmosphere – When/where do surface fluxes significantly affect boundary-layer growth, clouds and precipitation?

42/50


•“GLACE” (Koster et al 2004) coupling strength for summer soil moisture to rainfall (“hot spots”) corresponds to regions where there are both of these factors:

Land-Atmosphere Feedback via Two Legs

Paul Dirmeyer et al (George Mason Univ.)

43/50

∆P ∆SM g ∆ET g ∆P

Feedback path: Atmospheric leg Terrestrial leg

•High correlation between daily soil moisture and evapotranspiration during summer (from the Global Soil Moisture Wetness Project multi-model analysis, units are significance thresholds), and

•High CAPE (from North American Regional Reanalysis, J/kg)



1

2

3 1

4

4

3

2

relative humidity temperature

sensible heat flux

moisture flux

soil heat flux soil temperature soil moisture

downward longwave

cloud cover

boundary-layer growth

canopy conductance

*

incoming solar

surface layer & ABL

+positive feedback for C3 & C4 plants, negative feedback for CAM plants

land-surface processes

+

entrainment

above-ABL dryness

above-ABL stability

precipitation

reflected solar albedo

emitted longwave

surface temperature

turbulence

positive

negative

feedbacks:

8 8

5

5 6

6

7

7

radiation

*negative feedback above optimal temperature

wind

44/50

Local Land-Atmosphere Interactions

Local land-atmosphere interactions are an important part of large-scale terrestrial energy budget and global water cycle.


surface energy budget

transpiration canopy conductance

direct evaporation

surface-layer turb. exchange

boundary-layer growth

ABL-top dry-air entrainment

45/50


Near-Surface Interactions: Soil Moisture – Evapotranspiration Relationship

weak

str

ong

0.0 1.0 0.5

0.0

1.0

0.5

- stronger land-atmosphere coupling for forests.

deciduous forest

evergreen forest

- weaker land-atmosphere coupling for cropland and grassland.

cropland

grassland

• lower ef: strong coupling regardless of vegetation type: due to stronger surface heating and turbulence (larger ga, smaller gc).

deciduous forest

evergreen forest

cropland

grassland

Evaporative fraction versus “coupling strength” from surface flux observations from (“Fluxnet”):

• higher ef:

• Need to include effects of soil

hydraulics/thermodynamics.

Co

up

lin

g s

tren

gth

Evap. Frac.

46/50

Caterina Tassone, Michael Ek (NCEP/EMC)



Normalized RH tendency (Ek and Holtslag, 2004)

L

0.0 0.2 0.4 0.6 0.8 1.0

ne

-4

-2

0

2

4

1 1 1 1

0

0

00

2

2

2

-1

-1

-1

3

3

-2

-2

-3

-3

4

-4

-5

-4

-3

-2

-1

0

1

2

3

4

5

“normalized” RH tendency term

evaporative fraction (ef)

non-e

vapora

tive t

erm

s (

ne)

evaporative fraction (ef)

Land-Atmosphere Interactions: Soil moisture/Evaporation role in ABL & cloud development

47

Relative humidity tendency controls cloud initiation.

Non-evaporative term:

available energy term

dry-air entrainment

ABL growth

ABL warming

“normalized” tendency term

RH tendency with decreasing ef

RH tendency with increasing ef

surface moistening regime (stronger above-ABL stability and/or Dq large)

ABL-growth regime (weaker above-ABL stability and Dq small)

greatest RH tendency & ABL cloud potential.


Summary

• Land models provide surface boundary conditions for weather and climate models; requires proper representation of land-atmosphere interaction. Land important in its own right.

• For weather and climate modeling, land models must have valid (scale-aware) physics and associated parameters, proper atmospheric forcing, representative land data sets (in some cases near realtime/assimilated), initial & cycled land states.

• Longer time scales require more processes (physics), and longer-term land data sets, e.g. snow, vegetation fraction.

• Land model validation using (near-) surface observations, e.g. air temperature, relative humidity, wind, soil moisture, surface fluxes, etc… suggests model physics improvements; requires extensive data “mining” for processes-level studies.

“Get the right answers for the right reasons.”

• The role of land models is expanding in increasingly more fully-coupled Earth-System Models with connections between Weather & Climate and Hydrology, Ecosystems & Biogeo-chemical cycles (e.g. carbon), and Air and Water Quality.

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NCEP/EMC Land Team: Michael Ek, Jiarui Dong, Weizhong Zheng, Helin Wei, Jesse Meng, Youlong Xia, Rongqian Yang, Yihua Wu, Roshan Shrestha, Anil Kumar working with:

Land Data Assimilation (LDA), LDA Systems (e.g. “NLDAS”): • NASA/GSFC: Christa Peters-Lidard, David Mocko et al. • NCEP/Climate Prediction Center: Kingtse Mo et al. • Princeton University: Eric Wood, et al. • Univ. Washington: Dennis Lettenmaier (now UCLA), et al. • Michigan State Univ./formerly Princeton: Lifeng Luo.

Remotely-sensed Land Data Sets: •NESDIS/STAR land group: Ivan Csiszar, Xiwu Zhan (soil moisture), Bob Yu (Tskin, vegetation), Peter Romanov (snow), et al.

Land Model (Physics) Development: • NCAR/RAL: Fei Chen, Mike Barlage, et al. • Nat’l H2O Model: Brian Cosgrove (OWP), Dave Gochis (NCAR), et al. • Univ. Ariz.: Xubin Zeng et al. • UT-Austin: Zong-Liang Yang et al. • WRF land working group and NOAA/ESRL land model development:

Tanya Smirnova et al/NOAA/ESRL.

GEWEX/GLASS, GASS projects: Land model benchmarking, land- atmosphere interaction experiments with many international partners.

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Sampling of NCEP/EMC Land Team Partners ...requires broad community collaboration


50/50

Climate Models

Well… at least several more funding cycles. Best to book

it in for regular servicing.

Ugh! Look at the hydrology in this thing! It’s leaking

everywhere!

Climate modelers: But I like it like this… I don’t want to have to recalibrate my driving

variables (...what about my forecast metrics..!?)

So, how much will this cost?!

…you’re going to need an atmospheric

alignment to get the right interactions.

Uh oh! These surface fluxes don’t look so good.

…and its carbon emissions are way too

high...

Thank you!

Documents

Land Surface Physics - INPEsatelite.cptec.inpe.br/repositorio9/wcrp/... · 2nd WCRP Summer School on Climate Model Development [email protected] Cachoeira Paulista – SP, Brazil,